A predictive model for die roll height in fine blanking using machine learning methods

Stanke, Joachim; Feuerhack, Andreas; Trauth, Daniel; Mattfeld, Patrick; Klocke, Fritz

doi:10.1016/j.promfg.2018.07.279

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…Artificial Neural Network (ANN) models [38][39][40] consist of: (i) input layers, (ii) hidden layers, and (iii) output layers, Figure 2. Input layers are connected to the hidden layers by the weight functions (w ij ) which are calculated during the training of the ANN algorithm.…”

Section: Artificial Neural Network Methodsmentioning

confidence: 99%

“…The mandrel forming force integral over time, (FIOT), has been utilized as the output variable in the analysis, and as response value for the training and validation of the ML algorithms. Based on the most recent applications of machine learning model, eight different models have been adopted in the research presented in this paper, namely: linear methods [31,32], the kernel methods [33,34], the ensemble methods [35][36][37], and the artificial neural network (ANN) methodology [38][39][40], respectively.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Mirandola

Berti

Caracciolo

et al. 2021

Metals

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This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

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Section: Artificial Neural Network Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Mirandola

Berti

Caracciolo

et al. 2021

Metals

View full text Add to dashboard Cite

show abstract

“…7. The hidden layer has only one layer, the number of neurons is 12, and the number of neurons in the output layer is 1, which represents the length of the clean cutting surface [21] .…”

Section: Neural Network Modelingmentioning

confidence: 99%

A Novel Force Variation Fine Blanking Process for the High-strength and Low-plasticity Material

Mao

Chen

Liu

et al. 2021

Preprint

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Fine blanking is a kind of metal forming process with the advantages of high precision, good surface quality and low cost. Influenced by the concept of lightweight, a large number of metal materials with high strength are widely used in various fields. High strength materials are prone to be cracked during plastic deformation due to their poor plasticity, which limits the application range of them. This paper proposed a force variation fine blanking process for high-strength and low-plasticity materials. At the same time, a method to find the curve of forming force for this novel process was presented. A 2D finite element fine blanking model was established for the TC4 material. Combining genetic algorithm and neural network methods, a model was built up to find the optimal forming force loading curve. The parts fabricated by force variation loading and constant loading fine blanking process were compared through experiments. The mechanism of force variation fine blanking is also revealed. The forming force mainly affects the length of clean cutting surface by affecting hydrostatic stress. According to the ultimate optimal loading curve, the forming force should be kept at a low level in the early stage of blanking stroke, and increased gradually in the ending stage. In the application of force variation fine blanking, the part with long length of clean cutting surface can be obtained with lower die load.

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“…[7,10]); (iii) die roll height prediction in fine blanking (e.g. [43,48]); (iv) optimization of incremental sheet metal forming processes (e.g. [12,17,44]).…”

Section: Machine Learning Applications To Sheet Metal Formingmentioning

confidence: 99%

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Dib

Oliveira

Marques

et al. 2019

Neural Comput & Applic

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This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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A predictive model for die roll height in fine blanking using machine learning methods

Cited by 14 publications

References 11 publications

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

A Novel Force Variation Fine Blanking Process for the High-strength and Low-plasticity Material

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

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